Research

Grok 4.7 was evaluated on Senior SWE-Bench, our benchmark for measuring whether coding agents work like senior engineers. It reaches a tasteful pass@3 of 40.0%, up from 38.9% for Grok 4.6, ranking sixth overall at $0.24 per trial, which is a fraction of the cost compared to Opus 4.8. Benchmark Senior SWE-Bench evaluates agents as compared to a senior engineer.

Today, I’m excited to announce Snorkel’s $350M Series E financing at a $3.5B valuation, led by Insight and S32, with participation from Third Point, March, Blumberg, Allegis, Standard VC, Frontline, and existing investors Addition, Lightspeed, Greylock, GV, P7, Wells Fargo, Walden Catalyst Ventures, and Factory.

A student does not go from 1st to 12th grade in a single step. Each grade builds on a specific set of skills, and each one assumes the previous skills have already been mastered. Nobody learns calculus without algebra. When a student skips ahead anyway, what they end up with is memorization rather than understanding. The gaps show up later,

The speed of new frontier model releases keeps accelerating. Meanwhile benchmarks struggle to keep up and saturate quickly, often being left in the dust. Most benchmarks are static datasets with no active maintenance, causing them to lose value fast. Some benchmarks are looking to change this by becoming Continuous Benchmarks. Terminal-Bench is one of the most widely reported benchmarks on

Terminal-Bench 3.0 (formerly Frontier-Bench) recently launched, built to track what AI agents can and can’t do across real computer work. Terminal-Bench 2.1 has been saturating, with top agents reaching 84%; on Terminal-Bench 3.0, the best model, Claude Opus 5, achieves just 43.5%. Terminal-Bench 3.0 raises the bar with 74 authentic, verifiable tasks across 7 domains, designed to expose meaningful gaps

Anthropic’s Claude Opus 5 recently debuted as the second model overall on the current Senior SWE-bench leaderboard, behind Fable 5. It also achieves the highest score of any evaluated model on the benchmark’s Bug & Performance Investigation category, reinforcing the rapid progress frontier coding models continue to make on increasingly realistic software engineering tasks. Just as notable, Opus 5 reaches

Frontier-Bench launched this week – the successor to Terminal-Bench, built to track what AI agents can and can’t do across real computer work. Terminal-Bench 2.1 has been saturating, with top agents clearing 75-84%; on Frontier-Bench’s launch set of 74 tasks across 7 domains, the best mode Opus 5 achieving 43.3%. Snorkel AI contributed as a task author and data partner,

Most agent benchmarks still evaluate a thin slice of the job. The agent receives a task, produces an answer, gets scored, and the episode ends. Enterprise workflows work differently. An underwriting agent may need to read policy documents, inspect customer records, call internal tools, ask a simulated user for missing information, update state, and follow approval rules. A correct final

Long-horizon agents operate across many dependent states and transitions, often spanning multiple tools, environments, and periods of external feedback. The difficulty comes from preserving coherent progress as earlier decisions constrain later actions. A single workflow may involve researching evidence, changing files or records, waiting for external responses, revising plans, validating intermediate results, and returning to earlier systems with new information.

At our latest Snorkel AI Reading Group, Yiyou Sun and David (Xinyang) Han (UC Berkeley, Center for Responsible and Decentralized Intelligence) presented Agents’ Last Exam (ALE) — a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes. ALE is a collaboration between Berkeley RDI, Snorkel AI, and 300+ expert contributors across 55 professional subfields. ALE asks a deceptively simple question: can

Alex Ratner, co-founder and CEO of Snorkel AI, spoke at @Scale: Systems & Reliability about one of the most underappreciated problems in AI deployment: our ability to measure agents has been outpaced — arguably for the first time in the history of the field — by our ability to build them. The talk digs into what it actually takes to

For our third Benchtalks, the series dedicated to the researchers building the measurement toolkits that frontier labs hill-climb on, Snorkel AI co-founder Vincent Sunn Chen sat down with Parth Asawa, a PhD student at UC Berkeley advised by Matei Zaharia and Joey Gonzalez. Parth leads research on continual learning and is the creator of Continual Learning Bench, developed in collaboration

Most agent benchmarks evaluate each task as an independent episode. The agent receives a task, produces an answer, gets scored, and moves on. The next task starts as if the previous one never happened. That setup misses a core requirement for deployed agents. A coding agent, research assistant, data analyst, or workplace assistant should improve as it works across repeated

TL;DR We built a benchmark of 25 expert-authored KiCad schematic-editing tasks and ran a frontier computer-use agent against them. The headline numbers: 1. Why build a computer-use benchmark for electrical engineering? Most computer-use benchmarks today live in the same handful of apps: web browsers, file managers, generic productivity suites. Those evaluations are useful, but they share a structural weakness —

Vincent Sunn Chen spoke at AI Engineer London about what it actually takes to build benchmarks that move the field forward, not just measure it. The throughline is an asymmetry that keeps showing up across deployments and the 150+ proposals reviewed for the Open Benchmarks Grants: agent capabilities are climbing fast, but the ability to measure those agents in realistic,